CaltechAUTHORS
  A Caltech Library Service

DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems

Pan, Xiang and Chen, Minghua and Zhao, Tianyu and Low, Steven H. (2020) DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20200707-112147912

[img] PDF - Submitted Version
See Usage Policy.

673Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20200707-112147912

Abstract

The AC-OPF problem is the key and challenging problem in the power system operation. When solving the AC-OPF problem, the feasibility issue is critical. In this paper, we develop an efficient Deep Neural Network (DNN) approach, DeepOPF, to ensure the feasibility of the generated solution. The idea is to train a DNN model to predict a set of independent operating variables, and then to directly compute the remaining dependable variables by solving the AC power flow equations. While this guarantees the power-flow balances, the principal difficulty lies in ensuring that the obtained solutions satisfy the operation limits of generations, voltages, and branch flow. We tackle this hurdle by employing a penalty approach in training the DNN. As the penalty gradients make the common first-order gradient-based algorithms prohibited due to the hardness of obtaining an explicit-form expression of the penalty gradients, we further apply a zero-order optimization technique to design the training algorithm to address the critical issue. The simulation results of the IEEE test case demonstrate the effectiveness of the penalty approach. Also, they show that DeepOPF can speed up the computing time by one order of magnitude compared to a state-of-the-art solver, at the expense of minor optimality loss.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2007.01002arXivDiscussion Paper
ORCID:
AuthorORCID
Low, Steven H.0000-0001-6476-3048
Additional Information:We thank Andreas Venzke for the discussions related to the study presented in the paper.
Record Number:CaltechAUTHORS:20200707-112147912
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200707-112147912
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104249
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Jul 2020 18:44
Last Modified:07 Jul 2020 18:44

Repository Staff Only: item control page